A joint Gamma model is a statistical model of the following form. More...

#include <STK_ModelGamma_aj_bj.h>

Inheritance diagram for STK::ModelGamma_aj_bj< Data_, WColVector_ >:

Classes
class	dloglikelihood

Public Types
typedef DataBridge< Data_ >	Data
	Type of the container storing the data.

typedef hidden::Traits< Data_ >::Row	RowVector

typedef WColVector_	WColVector
	Type of the array storing the weights of the data.

typedef IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >	Base
	Type of the data in the container.

Public Types inherited from STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >
typedef hidden::StatModelTraits< ModelGamma_aj_bj< Data_, WColVector_ > >::Data	Data
	Type of the container with the data.

typedef hidden::StatModelTraits< ModelGamma_aj_bj< Data_, WColVector_ > >::Type	Type
	Type of the data in the container.

typedef hidden::StatModelTraits< ModelGamma_aj_bj< Data_, WColVector_ > >::WColVector	WColVector
	Type of the vector with the weights.

typedef hidden::StatModelTraits< ModelGamma_aj_bj< Data_, WColVector_ > >::Parameters	Parameters
	Type of the parameters of the Model.

Public Member Functions
	ModelGamma_aj_bj ()
	default constructor.

	ModelGamma_aj_bj (Data const &data)
	Constructor with data set.

	ModelGamma_aj_bj (Data const *p_data)
	Constructor with a ptr on the data set.

	ModelGamma_aj_bj (ModelGamma_aj_bj const &model)
	Copy constructor.

	~ModelGamma_aj_bj ()
	destructor

CPointX const &	shape () const

CPointX const &	scale () const

CPointX const &	mean () const

CPointX const &	meanLog () const
	vector of the mean log of the observations

CPointX const &	variance () const
	vector of the variance of the observations

int	computeNbFreeParameters () const
	compute the number of free parameters

Real	computeLnLikelihood (RowVector const &rowData) const
	compute the log Likelihood of an observation.

void	computeParameters ()
	compute the parameters

void	computeParameters (WColVector const &weights)
	compute the weighted parameters

void	writeParametersImpl (ostream &os) const
	Write the parameters on the output stream os.

Public Member Functions inherited from STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >
Data const *const	p_data () const

Parameters const &	param () const

String const &	error () const

void	setData (Data const *p_data)
	Set the data set.

bool	run ()
	Estimate the parameters of the model and update the model.

bool	run (WColVector const &weights)
	compute the weighted empirical probability of success based on the observed variables.

void	writeParameters (ostream &os)

void	writeParametersImpl (ostream &os)
	default implementation of the writeParameters method.

Public Member Functions inherited from STK::IStatModelBase
int	nbSample () const

Real	lnNbSample () const

int	nbVariable () const

Real	lnLikelihood () const

Real	likelihood () const

int	nbFreeParameter () const

Real	computeBIC () const

Real	computeAIC () const

Real	computeML () const

Public Member Functions inherited from STK::IRecursiveTemplate< Derived >
Derived &	asDerived ()
	static cast : return a reference of this with a cast to the derived class.

Derived const &	asDerived () const
	static cast : return a const reference of this with a cast to the derived class.

Derived *	asPtrDerived ()
	static cast : return a ptr on a `Derived` of this with a cast to the derived class.

Derived const *	asPtrDerived () const
	static cast : return a ptr on a constant `Derived` of this with a cast to the derived class.

Derived *	clone () const
	create a leaf using the copy constructor of the Derived class.

Derived *	clone (bool isRef) const
	create a leaf using the copy constructor of the Derived class and a flag determining if the clone is a reference or not.

Private Member Functions
CPointX &	shape ()

CPointX &	scale ()

CPointX &	mean ()

CPointX &	meanLog ()
	vector of the mean log of the observations

CPointX &	variance ()
	vector of the variance of the observations

Additional Inherited Members
Protected Member Functions inherited from STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >
	IMultiStatModel ()
	default constructor.

	IMultiStatModel (Data const &data)
	Constructor with data set.

	IMultiStatModel (Data const *p_data)
	Constructor with a ptr on the data set.

	IMultiStatModel (IMultiStatModel const &model)
	Copy constructor.

	~IMultiStatModel ()
	destructor

Parameters &	param ()

Real	computeLnLikelihood () const
	compute the log Likelihood of the statistical model.

void	update ()
	update the model if a new data set is set

Protected Member Functions inherited from STK::IStatModelBase
	IStatModelBase ()
	Default constructor.

	IStatModelBase (int nbSample)
	Constructor with specified dimension.

	IStatModelBase (int nbSample, int nbVariable)
	Constructor with specified dimension.

	IStatModelBase (IStatModelBase const &model)
	Copy constructor.

	~IStatModelBase ()
	destructor

void	setNbFreeParameter (int const &nbFreeParameter)
	set the number of free parameters of the model

void	setNbSample (int const &nbSample)
	set the number of samples of the model

void	setNbVariable (int const &nbVariable)
	set the number of variables of the model

void	setLnLikelihood (Real const &lnLikelihood)
	set the log-likelihood of the model

void	initialize (int nbSample, int nbVariable)
	set the dimensions of the parameters of the model

Protected Member Functions inherited from STK::IRecursiveTemplate< Derived >
	IRecursiveTemplate ()
	constructor.

	~IRecursiveTemplate ()
	destructor.

Protected Attributes inherited from STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >
Data const *	p_data_
	Pointer on the parameters of the model.

Parameters	param_
	Pointer on the parameters of the model.

String	msg_error_
	String with the last error message.

Detailed Description

template<class Data_, class WColVector_>
class STK::ModelGamma_aj_bj< Data_, WColVector_ >

A joint Gamma model is a statistical model of the following form.

$f(\mathbf{x}_i|\theta) = \prod_{j=1}^p\left(\frac{x_i^j}{b_j}\right)^{a_j-1} \frac{e^{-x_i^j/b_j}}{b_j \, \Gamma(a_j)}, \quad x_i^j\in\{0,1\}, \quad j=1,\ldots,p, \quad i=1,\ldots,n.$

Definition at line 142 of file STK_ModelGamma_aj_bj.h.

Member Typedef Documentation

◆ Base

template<class Data_ , class WColVector_ >

typedef IMultiStatModel< ModelGamma_aj_bj<Data_, WColVector_> > STK::ModelGamma_aj_bj< Data_, WColVector_ >::Base

Type of the data in the container.

Base class

Definition at line 171 of file STK_ModelGamma_aj_bj.h.

◆ Data

template<class Data_ , class WColVector_ >

typedef DataBridge<Data_> STK::ModelGamma_aj_bj< Data_, WColVector_ >::Data

Type of the container storing the data.

Definition at line 163 of file STK_ModelGamma_aj_bj.h.

◆ RowVector

template<class Data_ , class WColVector_ >

typedef hidden::Traits<Data_>::Row STK::ModelGamma_aj_bj< Data_, WColVector_ >::RowVector

Definition at line 164 of file STK_ModelGamma_aj_bj.h.

◆ WColVector

template<class Data_ , class WColVector_ >

typedef WColVector_ STK::ModelGamma_aj_bj< Data_, WColVector_ >::WColVector

Type of the array storing the weights of the data.

Definition at line 166 of file STK_ModelGamma_aj_bj.h.

Constructor & Destructor Documentation

◆ ModelGamma_aj_bj() [1/4]

template<class Data_ , class WColVector_ >

STK::ModelGamma_aj_bj< Data_, WColVector_ >::ModelGamma_aj_bj ( )

inline

default constructor.

Definition at line 176 of file STK_ModelGamma_aj_bj.h.

176: Base() {}

STK::ModelGamma_aj_bj::Base

IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > > Base

Type of the data in the container.

Definition STK_ModelGamma_aj_bj.h:171

◆ ModelGamma_aj_bj() [2/4]

template<class Data_ , class WColVector_ >

STK::ModelGamma_aj_bj< Data_, WColVector_ >::ModelGamma_aj_bj ( Data const & data )

inline

Constructor with data set.

Definition at line 178 of file STK_ModelGamma_aj_bj.h.

178: Base(data){}

◆ ModelGamma_aj_bj() [3/4]

template<class Data_ , class WColVector_ >

STK::ModelGamma_aj_bj< Data_, WColVector_ >::ModelGamma_aj_bj ( Data const * p_data )

inline

Constructor with a ptr on the data set.

Definition at line 180 of file STK_ModelGamma_aj_bj.h.

180: Base(p_data){}

STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::p_data

Data const *const p_data() const

Definition STK_IMultiStatModel.h:152

◆ ModelGamma_aj_bj() [4/4]

template<class Data_ , class WColVector_ >

STK::ModelGamma_aj_bj< Data_, WColVector_ >::ModelGamma_aj_bj ( ModelGamma_aj_bj< Data_, WColVector_ > const & model )

inline

Copy constructor.

Definition at line 182 of file STK_ModelGamma_aj_bj.h.

182: Base(model) {}

◆ ~ModelGamma_aj_bj()

template<class Data_ , class WColVector_ >

STK::ModelGamma_aj_bj< Data_, WColVector_ >::~ModelGamma_aj_bj ( )

inline

destructor

Definition at line 184 of file STK_ModelGamma_aj_bj.h.

184{}

Member Function Documentation

◆ computeLnLikelihood()

template<class Data_ , class WColVector_ >

Real STK::ModelGamma_aj_bj< Data_, WColVector_ >::computeLnLikelihood ( RowVector const & rowData ) const

compute the log Likelihood of an observation.

Definition at line 223 of file STK_ModelGamma_aj_bj.h.

{
  Real sum =0.;
  for (Integer j= rowData.begin(); j < rowData.end(); ++j)
  { sum += Law::Gamma::lpdf(rowData[j], shape()[j], scale()[j]);}
  return sum;
}

References STK::Law::Gamma::lpdf(), and STK::sum().

◆ computeNbFreeParameters()

template<class Data_ , class WColVector_ >

int STK::ModelGamma_aj_bj< Data_, WColVector_ >::computeNbFreeParameters ( ) const

inline

compute the number of free parameters

Definition at line 198 of file STK_ModelGamma_aj_bj.h.

198{ return 2*p_data()->dataij().sizeCols();}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::p_data().

◆ computeParameters() [1/2]

template<class Data_ , class WColVector_ >

void STK::ModelGamma_aj_bj< Data_, WColVector_ >::computeParameters ( )

compute the parameters

Definition at line 233 of file STK_ModelGamma_aj_bj.h.

{
  for (int j=p_data()->dataij().beginCols(); j < p_data()->dataij().endCols(); ++j)
  {
    mean()[j] =  p_data()->dataij().col(j).meanSafe();
    meanLog()[j] = p_data()->dataij().col(j).safe(1.).log().mean();
    variance()[j] = p_data()->dataij().col(j).safe().variance();
    Real x0 = (mean()[j]*mean()[j]) / variance()[j];
    Real x1 = 0.9*x0 +  0.05/(mean()[j] - meanLog()[j]);
    dloglikelihood funct(mean()[j], meanLog()[j]);
    Real a =  Algo::findZero(funct, x0, x1, 1e-08);
    // replace with moment estimate if needed
    if (!Arithmetic<Real>::isFinite(a)) { a =  mean()[j]*mean()[j]/variance()[j];}
    shape()[j] = a;
    scale()[j] = mean()[j]/a;
  }
}

References STK::Algo::findZero(), and STK::mean().

◆ computeParameters() [2/2]

template<class Data_ , class WColVector_ >

void STK::ModelGamma_aj_bj< Data_, WColVector_ >::computeParameters ( WColVector const & weights )

compute the weighted parameters

Definition at line 252 of file STK_ModelGamma_aj_bj.h.

{
  for (int j=p_data()->dataij().beginCols(); j < p_data()->dataij().endCols(); ++j)
  {
    mean()[j] =  p_data()->dataij().col(j).safe().wmean(weights);
    meanLog()[j] = p_data()->dataij().col(j).safe(1).log().wmean(weights);
    variance()[j] = p_data()->dataij().col(j).safe().wvariance(weights);
    Real x0 = (mean()[j]*mean()[j]) / variance()[j];
    Real x1 = 0.9*x0 +  0.05/(mean()[j] - meanLog()[j]);
    dloglikelihood funct(mean()[j], meanLog()[j]);
    Real a =  Algo::findZero(funct, x0, x1, 1e-08);
    // replace with moment estimate if needed
    if (!Arithmetic<Real>::isFinite(a)) { a =  mean()[j]*mean()[j]/variance()[j];}
    shape()[j] = a;
    scale()[j] = mean()[j]/a;
  }
}

References STK::Algo::findZero(), and STK::mean().

◆ mean() [1/2]

template<class Data_ , class WColVector_ >

CPointX & STK::ModelGamma_aj_bj< Data_, WColVector_ >::mean ( )

inlineprivate

Returns: the vector of the mean of the observations

Definition at line 214 of file STK_ModelGamma_aj_bj.h.

214{ return param().mean_;}

STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param

Parameters const & param() const

Definition STK_IMultiStatModel.h:154

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ mean() [2/2]

template<class Data_ , class WColVector_ >

CPointX const & STK::ModelGamma_aj_bj< Data_, WColVector_ >::mean ( ) const

inline

Returns: the vector of the mean of the observations

Definition at line 191 of file STK_ModelGamma_aj_bj.h.

191{return param().mean_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ meanLog() [1/2]

template<class Data_ , class WColVector_ >

CPointX & STK::ModelGamma_aj_bj< Data_, WColVector_ >::meanLog ( )

inlineprivate

vector of the mean log of the observations

Definition at line 216 of file STK_ModelGamma_aj_bj.h.

216{ return param().meanLog_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ meanLog() [2/2]

template<class Data_ , class WColVector_ >

CPointX const & STK::ModelGamma_aj_bj< Data_, WColVector_ >::meanLog ( ) const

inline

vector of the mean log of the observations

Definition at line 193 of file STK_ModelGamma_aj_bj.h.

193{return param().meanLog_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ scale() [1/2]

template<class Data_ , class WColVector_ >

CPointX & STK::ModelGamma_aj_bj< Data_, WColVector_ >::scale ( )

inlineprivate

Returns: the vector of the scale of the observations

Definition at line 212 of file STK_ModelGamma_aj_bj.h.

212{ return param().scale_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ scale() [2/2]

template<class Data_ , class WColVector_ >

CPointX const & STK::ModelGamma_aj_bj< Data_, WColVector_ >::scale ( ) const

inline

Returns: the vector of the mean of the observations

Definition at line 189 of file STK_ModelGamma_aj_bj.h.

189{return param().scale_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ shape() [1/2]

template<class Data_ , class WColVector_ >

CPointX & STK::ModelGamma_aj_bj< Data_, WColVector_ >::shape ( )

inlineprivate

Returns: the vector of the shape of the observations

Definition at line 210 of file STK_ModelGamma_aj_bj.h.

210{ return param().shape_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ shape() [2/2]

template<class Data_ , class WColVector_ >

CPointX const & STK::ModelGamma_aj_bj< Data_, WColVector_ >::shape ( ) const

inline

Returns: the vector of the mean of the observations

Definition at line 187 of file STK_ModelGamma_aj_bj.h.

187{return param().shape_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ variance() [1/2]

template<class Data_ , class WColVector_ >

CPointX & STK::ModelGamma_aj_bj< Data_, WColVector_ >::variance ( )

inlineprivate

vector of the variance of the observations

Definition at line 218 of file STK_ModelGamma_aj_bj.h.

218{ return param().variance_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ variance() [2/2]

template<class Data_ , class WColVector_ >

CPointX const & STK::ModelGamma_aj_bj< Data_, WColVector_ >::variance ( ) const

inline

vector of the variance of the observations

Definition at line 195 of file STK_ModelGamma_aj_bj.h.

195{return param().variance_;}

References STK::IMultiStatModel< ModelGamma_aj_bj< Data_, WColVector_ > >::param().

◆ writeParametersImpl()

template<class Data_ , class WColVector_ >

void STK::ModelGamma_aj_bj< Data_, WColVector_ >::writeParametersImpl ( ostream & os ) const

Write the parameters on the output stream os.

Definition at line 272 of file STK_ModelGamma_aj_bj.h.

{
  os << _T("shape = ") << shape();
  os << _T("scale = ") << scale();
}

References _T.

The documentation for this class was generated from the following file:

STK_ModelGamma_aj_bj.h

Classes

Public Types

Public Member Functions

Private Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ Data

◆ RowVector

◆ WColVector

Constructor & Destructor Documentation

◆ ModelGamma_aj_bj() [1/4]

◆ ModelGamma_aj_bj() [2/4]

◆ ModelGamma_aj_bj() [3/4]

◆ ModelGamma_aj_bj() [4/4]

◆ ~ModelGamma_aj_bj()

Member Function Documentation

◆ computeLnLikelihood()

◆ computeNbFreeParameters()

◆ computeParameters() [1/2]

◆ computeParameters() [2/2]

◆ mean() [1/2]

◆ mean() [2/2]

◆ meanLog() [1/2]

◆ meanLog() [2/2]

◆ scale() [1/2]

◆ scale() [2/2]

◆ shape() [1/2]

◆ shape() [2/2]

◆ variance() [1/2]

◆ variance() [2/2]

◆ writeParametersImpl()